{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agenda\n",
    "\n",
    "1. Introducing the bikeshare dataset\n",
    "    - Reading in the data\n",
    "    - Visualizing the data\n",
    "2. Linear regression basics\n",
    "    - Form of linear regression\n",
    "    - Building a linear regression model\n",
    "    - Using the model for prediction\n",
    "    - Does the scale of the features matter?\n",
    "3. Working with multiple features\n",
    "    - Visualizing the data (part 2)\n",
    "    - Adding more features to the model\n",
    "4. Choosing between models\n",
    "    - Feature selection\n",
    "    - Evaluation metrics for regression problems\n",
    "    - Comparing models with train/test split and RMSE\n",
    "    - Comparing testing RMSE with null RMSE\n",
    "5. Creating features\n",
    "    - Handling categorical features\n",
    "    - Feature engineering\n",
    "6. Comparing linear regression with other models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading in the data\n",
    "\n",
    "We'll be working with a dataset from Capital Bikeshare that was used in a Kaggle competition ([data dictionary](https://www.kaggle.com/c/bike-sharing-demand/data))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# read the data and set the datetime as the index\n",
    "import pandas as pd\n",
    "url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/bikeshare.csv'\n",
    "bikes = pd.read_csv(url, index_col='datetime', parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season  holiday  workingday  weather  temp   atemp  \\\n",
       "datetime                                                                  \n",
       "2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  count  \n",
       "datetime                                                             \n",
       "2011-01-01 00:00:00        81          0       3          13     16  \n",
       "2011-01-01 01:00:00        80          0       8          32     40  \n",
       "2011-01-01 02:00:00        80          0       5          27     32  \n",
       "2011-01-01 03:00:00        75          0       3          10     13  \n",
       "2011-01-01 04:00:00        75          0       0           1      1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Questions:**\n",
    "\n",
    "- What does each observation represent?\n",
    "- What is the response variable (as defined by Kaggle)?\n",
    "- How many features are there?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# \"count\" is a method, so it's best to name that column something else\n",
    "bikes.rename(columns={'count':'total'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (8, 6)\n",
    "plt.rcParams['font.size'] = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18266cc0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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qJ+dR2d7axhNO4Akn2N7aPsnjb5rs7e+zt7+PZZ7sh+8f5jDKbYxym/3D3AP3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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa88c828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pandas scatter plot\n",
    "bikes.plot(kind='scatter', x='temp', y='total', alpha=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1867af28>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jE3mxKxXjIMJxnxkmqqbIkMLYz4YOQm5MJAjCSOo2BGGEiR/CsQ1pUPzJY6bh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\niBNBRgNBEARBECeCjAaCIAiCIE7E/w/tKQAZZ4lE8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa88c5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn scatter plot with regression line\n",
    "sns.lmplot(x='temp', y='total', data=bikes, aspect=1.5, scatter_kws={'alpha':0.2})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Form of linear regression\n",
    "\n",
    "$y = \\beta_0 + \\beta_1x_1 + \\beta_2x_2 + ... + \\beta_nx_n$\n",
    "\n",
    "- $y$ is the response\n",
    "- $\\beta_0$ is the intercept\n",
    "- $\\beta_1$ is the coefficient for $x_1$ (the first feature)\n",
    "- $\\beta_n$ is the coefficient for $x_n$ (the nth feature)\n",
    "\n",
    "The $\\beta$ values are called the **model coefficients**:\n",
    "\n",
    "- These values are estimated (or \"learned\") during the model fitting process using the **least squares criterion**.\n",
    "- Specifically, we are find the line (mathematically) which minimizes the **sum of squared residuals** (or \"sum of squared errors\").\n",
    "- And once we've learned these coefficients, we can use the model to predict the response.\n",
    "\n",
    "![Estimating coefficients](images/estimating_coefficients.png)\n",
    "\n",
    "In the diagram above:\n",
    "\n",
    "- The black dots are the **observed values** of x and y.\n",
    "- The blue line is our **least squares line**.\n",
    "- The red lines are the **residuals**, which are the vertical distances between the observed values and the least squares line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building a linear regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create X and y\n",
    "feature_cols = ['temp']\n",
    "X = bikes[feature_cols]\n",
    "y = bikes.total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import, instantiate, fit\n",
    "from sklearn.linear_model import LinearRegression\n",
    "linreg = LinearRegression()\n",
    "linreg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.04621295961\n",
      "[ 9.17054048]\n"
     ]
    }
   ],
   "source": [
    "# print the coefficients\n",
    "print linreg.intercept_\n",
    "print linreg.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpreting the **intercept** ($\\beta_0$):\n",
    "\n",
    "- It is the value of $y$ when $x$=0.\n",
    "- Thus, it is the estimated number of rentals when the temperature is 0 degrees Celsius.\n",
    "- **Note:** It does not always make sense to interpret the intercept. (Why?)\n",
    "\n",
    "Interpreting the **\"temp\" coefficient** ($\\beta_1$):\n",
    "\n",
    "- It is the change in $y$ divided by change in $x$, or the \"slope\".\n",
    "- Thus, a temperature increase of 1 degree Celsius is **associated with** a rental increase of 9.17 bikes.\n",
    "- This is not a statement of causation.\n",
    "- $\\beta_1$ would be **negative** if an increase in temperature was associated with a **decrease** in rentals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the model for prediction\n",
    "\n",
    "How many bike rentals would we predict if the temperature was 25 degrees Celsius?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 235.309725])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# manually calculate the prediction\n",
    "linreg.intercept_ + linreg.coef_*25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 235.309725])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use the predict method\n",
    "linreg.predict(25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Does the scale of the features matter?\n",
    "\n",
    "Let's say that temperature was measured in Fahrenheit, rather than Celsius. How would that affect the model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>total</th>\n",
       "      <th>temp_F</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>49.712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>48.236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>48.236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>49.712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49.712</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season  holiday  workingday  weather  temp   atemp  \\\n",
       "datetime                                                                  \n",
       "2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  total  temp_F  \n",
       "datetime                                                                     \n",
       "2011-01-01 00:00:00        81          0       3          13     16  49.712  \n",
       "2011-01-01 01:00:00        80          0       8          32     40  48.236  \n",
       "2011-01-01 02:00:00        80          0       5          27     32  48.236  \n",
       "2011-01-01 03:00:00        75          0       3          10     13  49.712  \n",
       "2011-01-01 04:00:00        75          0       0           1      1  49.712  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a new column for Fahrenheit temperature\n",
    "bikes['temp_F'] = bikes.temp * 1.8 + 32\n",
    "bikes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1845fef0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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WKu+E6wbwfbaR6ybDBmyl78kqCpaoKSZXFedDVxpItZqCx/e7iWO/cdhBp8mu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\n/n8cMZ03Iy84BAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1843b160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn scatter plot with regression line\n",
    "sns.lmplot(x='temp_F', y='total', data=bikes, aspect=1.5, scatter_kws={'alpha':0.2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-156.985617821\n",
      "[ 5.09474471]\n"
     ]
    }
   ],
   "source": [
    "# create X and y\n",
    "feature_cols = ['temp_F']\n",
    "X = bikes[feature_cols]\n",
    "y = bikes.total\n",
    "\n",
    "# instantiate and fit\n",
    "linreg = LinearRegression()\n",
    "linreg.fit(X, y)\n",
    "\n",
    "# print the coefficients\n",
    "print linreg.intercept_\n",
    "print linreg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert 25 degrees Celsius to Fahrenheit\n",
    "25 * 1.8 + 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 235.309725])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict rentals for 77 degrees Fahrenheit\n",
    "linreg.predict(77)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion:** The scale of the features is **irrelevant** for linear regression models. When changing the scale, we simply change our **interpretation** of the coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# remove the temp_F column\n",
    "bikes.drop('temp_F', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the data (part 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# explore more features\n",
    "feature_cols = ['temp', 'season', 'weather', 'humidity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x18266c88>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f4f6BLi6ONKrvZubLDan/fv+uV85PMuc5zmdzZU68PsqjB/s5ONDNs54B2pLU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\n/wPgsszcAwdOdwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a4e1710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# multiple scatter plots in Seaborn\n",
    "sns.pairplot(bikes, x_vars=feature_cols, y_vars='total', kind='reg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zcUANXAD8gOXA3QP+3oElIPYB2hxsF4aJ9SQIGGOSsuIB/XeJNgBX11fodDp2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Ddd9WfQ+AV52JOLPDjLW+5cLpGh9ioUCiWZd3wOgNaKY8xNDMhtTUFRqdSf/xmRSbmEJ+bS31wq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\n3vlpBEEgLqOUxJxKwmOSMa7g7/B6vXjcTh7UnuJB7Sk8bide7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AqCgMEcjix7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DBvI3bYvr30j5kKVtsJmUo42X3+xzNNf52UgMUI5ulvX1dYHe7l4VXVEbAVxDaG+u+ro579fm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1acd96a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# multiple scatter plots in Pandas\n",
    "fig, axs = plt.subplots(1, len(feature_cols), sharey=True)\n",
    "for index, feature in enumerate(feature_cols):\n",
    "    bikes.plot(kind='scatter', x=feature, y='total', ax=axs[index], figsize=(16, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are you seeing anything that you did not expect?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>884</td>\n",
       "      <td>901</td>\n",
       "      <td>901</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>909</td>\n",
       "      <td>912</td>\n",
       "      <td>912</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>912</td>\n",
       "      <td>912</td>\n",
       "      <td>909</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>911</td>\n",
       "      <td>911</td>\n",
       "      <td>912</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0    1    2    3    4    5    6    7    8    9    10   11   12\n",
       "season                                                            \n",
       "1       884  901  901    0    0    0    0    0    0    0    0    0\n",
       "2         0    0    0  909  912  912    0    0    0    0    0    0\n",
       "3         0    0    0    0    0    0  912  912  909    0    0    0\n",
       "4         0    0    0    0    0    0    0    0    0  911  911  912"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cross-tabulation of season and month\n",
    "pd.crosstab(bikes.season, bikes.index.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1844f7b8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PY9my5ZPelrZNO/vyF+vvo6+vb9KnFbovO8u/y3r4dzmxwcGBvvGWd7pinw+s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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c260f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box plot of rentals, grouped by season\n",
    "bikes.boxplot(column='total', by='season')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notably:\n",
    "\n",
    "- A line can't capture a non-linear relationship.\n",
    "- There are more rentals in winter than in spring (?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c6186d8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PJ0QKiRFd9bu36OlNvUUrlTUxnHjljfRGJEcCeQYzeS9U1Jl/3rg1vFBIItJ9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\nPzWPSWNL6Oru48FXNUs2VjJpbDHXfvY07n5uE5+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c629cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# line plot of rentals\n",
    "bikes.total.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does this tell us?\n",
    "\n",
    "There are more rentals in the winter than the spring, but only because the system is experiencing **overall growth** and the winter months happen to come after the spring months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>season</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.029368</td>\n",
       "      <td>-0.008126</td>\n",
       "      <td>0.008879</td>\n",
       "      <td>0.258689</td>\n",
       "      <td>0.264744</td>\n",
       "      <td>0.190610</td>\n",
       "      <td>-0.147121</td>\n",
       "      <td>0.096758</td>\n",
       "      <td>0.164011</td>\n",
       "      <td>0.163439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>holiday</th>\n",
       "      <td>0.029368</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.250491</td>\n",
       "      <td>-0.007074</td>\n",
       "      <td>0.000295</td>\n",
       "      <td>-0.005215</td>\n",
       "      <td>0.001929</td>\n",
       "      <td>0.008409</td>\n",
       "      <td>0.043799</td>\n",
       "      <td>-0.020956</td>\n",
       "      <td>-0.005393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workingday</th>\n",
       "      <td>-0.008126</td>\n",
       "      <td>-0.250491</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033772</td>\n",
       "      <td>0.029966</td>\n",
       "      <td>0.024660</td>\n",
       "      <td>-0.010880</td>\n",
       "      <td>0.013373</td>\n",
       "      <td>-0.319111</td>\n",
       "      <td>0.119460</td>\n",
       "      <td>0.011594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weather</th>\n",
       "      <td>0.008879</td>\n",
       "      <td>-0.007074</td>\n",
       "      <td>0.033772</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.055035</td>\n",
       "      <td>-0.055376</td>\n",
       "      <td>0.406244</td>\n",
       "      <td>0.007261</td>\n",
       "      <td>-0.135918</td>\n",
       "      <td>-0.109340</td>\n",
       "      <td>-0.128655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp</th>\n",
       "      <td>0.258689</td>\n",
       "      <td>0.000295</td>\n",
       "      <td>0.029966</td>\n",
       "      <td>-0.055035</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.984948</td>\n",
       "      <td>-0.064949</td>\n",
       "      <td>-0.017852</td>\n",
       "      <td>0.467097</td>\n",
       "      <td>0.318571</td>\n",
       "      <td>0.394454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atemp</th>\n",
       "      <td>0.264744</td>\n",
       "      <td>-0.005215</td>\n",
       "      <td>0.024660</td>\n",
       "      <td>-0.055376</td>\n",
       "      <td>0.984948</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.043536</td>\n",
       "      <td>-0.057473</td>\n",
       "      <td>0.462067</td>\n",
       "      <td>0.314635</td>\n",
       "      <td>0.389784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>humidity</th>\n",
       "      <td>0.190610</td>\n",
       "      <td>0.001929</td>\n",
       "      <td>-0.010880</td>\n",
       "      <td>0.406244</td>\n",
       "      <td>-0.064949</td>\n",
       "      <td>-0.043536</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.318607</td>\n",
       "      <td>-0.348187</td>\n",
       "      <td>-0.265458</td>\n",
       "      <td>-0.317371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windspeed</th>\n",
       "      <td>-0.147121</td>\n",
       "      <td>0.008409</td>\n",
       "      <td>0.013373</td>\n",
       "      <td>0.007261</td>\n",
       "      <td>-0.017852</td>\n",
       "      <td>-0.057473</td>\n",
       "      <td>-0.318607</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.092276</td>\n",
       "      <td>0.091052</td>\n",
       "      <td>0.101369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>casual</th>\n",
       "      <td>0.096758</td>\n",
       "      <td>0.043799</td>\n",
       "      <td>-0.319111</td>\n",
       "      <td>-0.135918</td>\n",
       "      <td>0.467097</td>\n",
       "      <td>0.462067</td>\n",
       "      <td>-0.348187</td>\n",
       "      <td>0.092276</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.497250</td>\n",
       "      <td>0.690414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>registered</th>\n",
       "      <td>0.164011</td>\n",
       "      <td>-0.020956</td>\n",
       "      <td>0.119460</td>\n",
       "      <td>-0.109340</td>\n",
       "      <td>0.318571</td>\n",
       "      <td>0.314635</td>\n",
       "      <td>-0.265458</td>\n",
       "      <td>0.091052</td>\n",
       "      <td>0.497250</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.970948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total</th>\n",
       "      <td>0.163439</td>\n",
       "      <td>-0.005393</td>\n",
       "      <td>0.011594</td>\n",
       "      <td>-0.128655</td>\n",
       "      <td>0.394454</td>\n",
       "      <td>0.389784</td>\n",
       "      <td>-0.317371</td>\n",
       "      <td>0.101369</td>\n",
       "      <td>0.690414</td>\n",
       "      <td>0.970948</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              season   holiday  workingday   weather      temp     atemp  \\\n",
       "season      1.000000  0.029368   -0.008126  0.008879  0.258689  0.264744   \n",
       "holiday     0.029368  1.000000   -0.250491 -0.007074  0.000295 -0.005215   \n",
       "workingday -0.008126 -0.250491    1.000000  0.033772  0.029966  0.024660   \n",
       "weather     0.008879 -0.007074    0.033772  1.000000 -0.055035 -0.055376   \n",
       "temp        0.258689  0.000295    0.029966 -0.055035  1.000000  0.984948   \n",
       "atemp       0.264744 -0.005215    0.024660 -0.055376  0.984948  1.000000   \n",
       "humidity    0.190610  0.001929   -0.010880  0.406244 -0.064949 -0.043536   \n",
       "windspeed  -0.147121  0.008409    0.013373  0.007261 -0.017852 -0.057473   \n",
       "casual      0.096758  0.043799   -0.319111 -0.135918  0.467097  0.462067   \n",
       "registered  0.164011 -0.020956    0.119460 -0.109340  0.318571  0.314635   \n",
       "total       0.163439 -0.005393    0.011594 -0.128655  0.394454  0.389784   \n",
       "\n",
       "            humidity  windspeed    casual  registered     total  \n",
       "season      0.190610  -0.147121  0.096758    0.164011  0.163439  \n",
       "holiday     0.001929   0.008409  0.043799   -0.020956 -0.005393  \n",
       "workingday -0.010880   0.013373 -0.319111    0.119460  0.011594  \n",
       "weather     0.406244   0.007261 -0.135918   -0.109340 -0.128655  \n",
       "temp       -0.064949  -0.017852  0.467097    0.318571  0.394454  \n",
       "atemp      -0.043536  -0.057473  0.462067    0.314635  0.389784  \n",
       "humidity    1.000000  -0.318607 -0.348187   -0.265458 -0.317371  \n",
       "windspeed  -0.318607   1.000000  0.092276    0.091052  0.101369  \n",
       "casual     -0.348187   0.092276  1.000000    0.497250  0.690414  \n",
       "registered -0.265458   0.091052  0.497250    1.000000  0.970948  \n",
       "total      -0.317371   0.101369  0.690414    0.970948  1.000000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# correlation matrix (ranges from 1 to -1)\n",
    "bikes.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c725358>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LMCar4WZOc/efZV0GERFpTvUcYGZmR/D52vSHwJS4PZW5V1VVXrMxcBCwP+Fy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c725e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize correlation matrix in Seaborn using a heatmap\n",
    "sns.heatmap(bikes.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What relationships do you notice?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding more features to the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a list of features\n",
    "feature_cols = ['temp', 'season', 'weather', 'humidity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "159.520687861\n",
      "[  7.86482499  22.53875753   6.67030204  -3.11887338]\n"
     ]
    }
   ],
   "source": [
    "# create X and y\n",
    "X = bikes[feature_cols]\n",
    "y = bikes.total\n",
    "\n",
    "# instantiate and fit\n",
    "linreg = LinearRegression()\n",
    "linreg.fit(X, y)\n",
    "\n",
    "# print the coefficients\n",
    "print linreg.intercept_\n",
    "print linreg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('temp', 7.8648249924775815),\n",
       " ('season', 22.53875753246696),\n",
       " ('weather', 6.6703020359238963),\n",
       " ('humidity', -3.1188733823965133)]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pair the feature names with the coefficients\n",
    "zip(feature_cols, linreg.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpreting the coefficients:\n",
    "\n",
    "- Holding all other features fixed, a 1 unit increase in **temperature** is associated with a **rental increase of 7.86 bikes**.\n",
    "- Holding all other features fixed, a 1 unit increase in **season** is associated with a **rental increase of 22.5 bikes**.\n",
    "- Holding all other features fixed, a 1 unit increase in **weather** is associated with a **rental increase of 6.67 bikes**.\n",
    "- Holding all other features fixed, a 1 unit increase in **humidity** is associated with a **rental decrease of 3.12 bikes**.\n",
    "\n",
    "Does anything look incorrect?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature selection\n",
    "\n",
    "How do we choose which features to include in the model? We're going to use **train/test split** (and eventually **cross-validation**).\n",
    "\n",
    "Why not use of **p-values** or **R-squared** for feature selection?\n",
    "\n",
    "- Linear models rely upon **a lot of assumptions** (such as the features being independent), and if those assumptions are violated, p-values and R-squared are less reliable. Train/test split relies on fewer assumptions.\n",
    "- Features that are unrelated to the response can still have **significant p-values**.\n",
    "- Adding features to your model that are unrelated to the response will always **increase the R-squared value**, and adjusted R-squared does not sufficiently account for this.\n",
    "- p-values and R-squared are **proxies** for our goal of generalization, whereas train/test split and cross-validation attempt to **directly estimate** how well the model will generalize to out-of-sample data.\n",
    "\n",
    "More generally:\n",
    "\n",
    "- There are different methodologies that can be used for solving any given data science problem, and this course follows a **machine learning methodology**.\n",
    "- This course focuses on **general purpose approaches** that can be applied to any model, rather than model-specific approaches."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation metrics for regression problems\n",
    "\n",
    "Evaluation metrics for classification problems, such as **accuracy**, are not useful for regression problems. We need evaluation metrics designed for comparing **continuous values**.\n",
    "\n",
    "Here are three common evaluation metrics for regression problems:\n",
    "\n",
    "**Mean Absolute Error** (MAE) is the mean of the absolute value of the errors:\n",
    "\n",
    "$$\\frac 1n\\sum_{i=1}^n|y_i-\\hat{y}_i|$$\n",
    "\n",
    "**Mean Squared Error** (MSE) is the mean of the squared errors:\n",
    "\n",
    "$$\\frac 1n\\sum_{i=1}^n(y_i-\\hat{y}_i)^2$$\n",
    "\n",
    "**Root Mean Squared Error** (RMSE) is the square root of the mean of the squared errors:\n",
    "\n",
    "$$\\sqrt{\\frac 1n\\sum_{i=1}^n(y_i-\\hat{y}_i)^2}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# example true and predicted response values\n",
    "true = [10, 7, 5, 5]\n",
    "pred = [8, 6, 5, 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 2.0\n",
      "MSE: 7.5\n",
      "RMSE: 2.73861278753\n"
     ]
    }
   ],
   "source": [
    "# calculate these metrics by hand!\n",
    "from sklearn import metrics\n",
    "import numpy as np\n",
    "print 'MAE:', metrics.mean_absolute_error(true, pred)\n",
    "print 'MSE:', metrics.mean_squared_error(true, pred)\n",
    "print 'RMSE:', np.sqrt(metrics.mean_squared_error(true, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing these metrics:\n",
    "\n",
    "- **MAE** is the easiest to understand, because it's the average error.\n",
    "- **MSE** is more popular than MAE, because MSE \"punishes\" larger errors, which tends to be useful in the real world.\n",
    "- **RMSE** is even more popular than MSE, because RMSE is interpretable in the \"y\" units.\n",
    "\n",
    "All of these are **loss functions**, because we want to minimize them.\n",
    "\n",
    "Here's an additional example, to demonstrate how MSE/RMSE punish larger errors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 2.0\n",
      "MSE: 16.0\n",
      "RMSE: 4.0\n"
     ]
    }
   ],
   "source": [
    "# same true values as above\n",
    "true = [10, 7, 5, 5]\n",
    "\n",
    "# new set of predicted values\n",
    "pred = [10, 7, 5, 13]\n",
    "\n",
    "# MAE is the same as before\n",
    "print 'MAE:', metrics.mean_absolute_error(true, pred)\n",
    "\n",
    "# MSE and RMSE are larger than before\n",
    "print 'MSE:', metrics.mean_squared_error(true, pred)\n",
    "print 'RMSE:', np.sqrt(metrics.mean_squared_error(true, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing models with train/test split and RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "# define a function that accepts a list of features and returns testing RMSE\n",
    "def train_test_rmse(feature_cols):\n",
    "    X = bikes[feature_cols]\n",
    "    y = bikes.total\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=123)\n",
    "    linreg = LinearRegression()\n",
    "    linreg.fit(X_train, y_train)\n",
    "    y_pred = linreg.predict(X_test)\n",
    "    return np.sqrt(metrics.mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "155.649459131\n",
      "164.165399763\n",
      "155.598189367\n"
     ]
    }
   ],
   "source": [
    "# compare different sets of features\n",
    "print train_test_rmse(['temp', 'season', 'weather', 'humidity'])\n",
    "print train_test_rmse(['temp', 'season', 'weather'])\n",
    "print train_test_rmse(['temp', 'season', 'humidity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.35448917777e-13\n"
     ]
    }
   ],
   "source": [
    "# using these as features is not allowed!\n",
    "print train_test_rmse(['casual', 'registered'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing testing RMSE with null RMSE\n",
    "\n",
    "Null RMSE is the RMSE that could be achieved by **always predicting the mean response value**. It is a benchmark against which you may want to measure your regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 192.26451139,  192.26451139,  192.26451139, ...,  192.26451139,\n",
       "        192.26451139,  192.26451139])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split X and y into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=123)\n",
    "\n",
    "# create a NumPy array with the same shape as y_test\n",
    "y_null = np.zeros_like(y_test, dtype=float)\n",
    "\n",
    "# fill the array with the mean value of y_test\n",
    "y_null.fill(y_test.mean())\n",
    "y_null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179.57906896465727"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute null RMSE\n",
    "np.sqrt(metrics.mean_squared_error(y_test, y_null))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling categorical features\n",
    "\n",
    "scikit-learn expects all features to be numeric. So how do we include a categorical feature in our model?\n",
    "\n",
    "- **Ordered categories:** transform them to sensible numeric values (example: small=1, medium=2, large=3)\n",
    "- **Unordered categories:** use dummy encoding (0/1)\n",
    "\n",
    "What are the categorical features in our dataset?\n",
    "\n",
    "- **Ordered categories:** weather (already encoded with sensible numeric values)\n",
    "- **Unordered categories:** season (needs dummy encoding), holiday (already dummy encoded), workingday (already dummy encoded)\n",
    "\n",
    "For season, we can't simply leave the encoding as 1 = spring, 2 = summer, 3 = fall, and 4 = winter, because that would imply an **ordered relationship**. Instead, we create **multiple dummy variables:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season_1</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-09-05 11:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-18 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-14 17:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-04 15:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-11 02:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season_1  season_2  season_3  season_4\n",
       "datetime                                                   \n",
       "2011-09-05 11:00:00         0         0         1         0\n",
       "2012-03-18 04:00:00         1         0         0         0\n",
       "2012-10-14 17:00:00         0         0         0         1\n",
       "2011-04-04 15:00:00         0         1         0         0\n",
       "2012-12-11 02:00:00         0         0         0         1"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create dummy variables\n",
    "season_dummies = pd.get_dummies(bikes.season, prefix='season')\n",
    "\n",
    "# print 5 random rows\n",
    "season_dummies.sample(n=5, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, we actually only need **three dummy variables (not four)**, and thus we'll drop the first dummy variable.\n",
    "\n",
    "Why? Because three dummies captures all of the \"information\" about the season feature, and implicitly defines spring (season 1) as the **baseline level:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-09-05 11:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-18 04:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-14 17:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-04 15:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-11 02:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season_2  season_3  season_4\n",
       "datetime                                         \n",
       "2011-09-05 11:00:00         0         1         0\n",
       "2012-03-18 04:00:00         0         0         0\n",
       "2012-10-14 17:00:00         0         0         1\n",
       "2011-04-04 15:00:00         1         0         0\n",
       "2012-12-11 02:00:00         0         0         1"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop the first column\n",
    "season_dummies.drop(season_dummies.columns[0], axis=1, inplace=True)\n",
    "\n",
    "# print 5 random rows\n",
    "season_dummies.sample(n=5, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, if you have a categorical feature with **k possible values**, you create **k-1 dummy variables**.\n",
    "\n",
    "If that's confusing, think about why we only need one dummy variable for holiday, not two dummy variables (holiday_yes and holiday_no)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>total</th>\n",
       "      <th>season_2</th>\n",
       "      <th>season_3</th>\n",
       "      <th>season_4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-09-05 11:00:00</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>28.70</td>\n",
       "      <td>33.335</td>\n",
       "      <td>74</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>101</td>\n",
       "      <td>207</td>\n",
       "      <td>308</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-18 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17.22</td>\n",
       "      <td>21.210</td>\n",
       "      <td>94</td>\n",
       "      <td>11.0014</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-14 17:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26.24</td>\n",
       "      <td>31.060</td>\n",
       "      <td>44</td>\n",
       "      <td>12.9980</td>\n",
       "      <td>193</td>\n",
       "      <td>346</td>\n",
       "      <td>539</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-04 15:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>31.16</td>\n",
       "      <td>33.335</td>\n",
       "      <td>23</td>\n",
       "      <td>36.9974</td>\n",
       "      <td>47</td>\n",
       "      <td>96</td>\n",
       "      <td>143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-11 02:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>16.40</td>\n",
       "      <td>20.455</td>\n",
       "      <td>66</td>\n",
       "      <td>22.0028</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season  holiday  workingday  weather   temp   atemp  \\\n",
       "datetime                                                                   \n",
       "2011-09-05 11:00:00       3        1           0        2  28.70  33.335   \n",
       "2012-03-18 04:00:00       1        0           0        2  17.22  21.210   \n",
       "2012-10-14 17:00:00       4        0           0        1  26.24  31.060   \n",
       "2011-04-04 15:00:00       2        0           1        1  31.16  33.335   \n",
       "2012-12-11 02:00:00       4        0           1        2  16.40  20.455   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  total  season_2  \\\n",
       "datetime                                                                        \n",
       "2011-09-05 11:00:00        74    11.0014     101         207    308         0   \n",
       "2012-03-18 04:00:00        94    11.0014       6           8     14         0   \n",
       "2012-10-14 17:00:00        44    12.9980     193         346    539         0   \n",
       "2011-04-04 15:00:00        23    36.9974      47          96    143         1   \n",
       "2012-12-11 02:00:00        66    22.0028       0           1      1         0   \n",
       "\n",
       "                     season_3  season_4  \n",
       "datetime                                 \n",
       "2011-09-05 11:00:00         1         0  \n",
       "2012-03-18 04:00:00         0         0  \n",
       "2012-10-14 17:00:00         0         1  \n",
       "2011-04-04 15:00:00         0         0  \n",
       "2012-12-11 02:00:00         0         1  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concatenate the original DataFrame and the dummy DataFrame (axis=0 means rows, axis=1 means columns)\n",
    "bikes = pd.concat([bikes, season_dummies], axis=1)\n",
    "\n",
    "# print 5 random rows\n",
    "bikes.sample(n=5, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('temp', 11.186405863576075),\n",
       " ('season_2', -3.3905430997197659),\n",
       " ('season_3', -41.736860713173307),\n",
       " ('season_4', 64.415961468240582),\n",
       " ('humidity', -2.8194816362596531)]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# include dummy variables for season in the model\n",
    "feature_cols = ['temp', 'season_2', 'season_3', 'season_4', 'humidity']\n",
    "X = bikes[feature_cols]\n",
    "y = bikes.total\n",
    "linreg = LinearRegression()\n",
    "linreg.fit(X, y)\n",
    "zip(feature_cols, linreg.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do we interpret the season coefficients? They are **measured against the baseline (spring)**:\n",
    "\n",
    "- Holding all other features fixed, **summer** is associated with a **rental decrease of 3.39 bikes** compared to the spring.\n",
    "- Holding all other features fixed, **fall** is associated with a **rental decrease of 41.7 bikes** compared to the spring.\n",
    "- Holding all other features fixed, **winter** is associated with a **rental increase of 64.4 bikes** compared to the spring.\n",
    "\n",
    "Would it matter if we changed which season was defined as the baseline?\n",
    "\n",
    "- No, it would simply change our **interpretation** of the coefficients.\n",
    "\n",
    "**Important:** Dummy encoding is relevant for all machine learning models, not just linear regression models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "155.598189367\n",
      "154.333945936\n"
     ]
    }
   ],
   "source": [
    "# compare original season variable with dummy variables\n",
    "print train_test_rmse(['temp', 'season', 'humidity'])\n",
    "print train_test_rmse(['temp', 'season_2', 'season_3', 'season_4', 'humidity'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature engineering\n",
    "\n",
    "See if you can create the following features:\n",
    "\n",
    "- **hour:** as a single numeric feature (0 through 23)\n",
    "- **hour:** as a categorical feature (use 23 dummy variables)\n",
    "- **daytime:** as a single categorical feature (daytime=1 from 7am to 8pm, and daytime=0 otherwise)\n",
    "\n",
    "Then, try using each of the three features (on its own) with `train_test_rmse` to see which one performs the best!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# hour as a numeric feature\n",
    "bikes['hour'] = bikes.index.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# hour as a categorical feature\n",
    "hour_dummies = pd.get_dummies(bikes.hour, prefix='hour')\n",
    "hour_dummies.drop(hour_dummies.columns[0], axis=1, inplace=True)\n",
    "bikes = pd.concat([bikes, hour_dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# daytime as a categorical feature\n",
    "bikes['daytime'] = ((bikes.hour > 6) & (bikes.hour < 21)).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "165.671742579\n",
      "128.311205028\n",
      "144.891163626\n"
     ]
    }
   ],
   "source": [
    "print train_test_rmse(['hour'])\n",
    "print train_test_rmse(bikes.columns[bikes.columns.str.startswith('hour_')])\n",
    "print train_test_rmse(['daytime'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing linear regression with other models\n",
    "\n",
    "Advantages of linear regression:\n",
    "\n",
    "- Simple to explain\n",
    "- Highly interpretable\n",
    "- Model training and prediction are fast\n",
    "- No tuning is required (excluding regularization)\n",
    "- Features don't need scaling\n",
    "- Can perform well with a small number of observations\n",
    "- Well-understood\n",
    "\n",
    "Disadvantages of linear regression:\n",
    "\n",
    "- Presumes a linear relationship between the features and the response\n",
    "- Performance is (generally) not competitive with the best supervised learning methods due to high bias\n",
    "- Can't automatically learn feature interactions"
   ]
  }
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